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Magnetic Resonance Imaging

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Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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The number of nuclear spins aligned in the lower energy state is slightly greater than those in the higher energy state. In the presence of an external magnetic field, as the spins precess at the Larmor frequency, the excess population results in a net magnetization oriented along the z axis. When a pulse or a short burst of radio waves at the Larmor frequency is applied along the x axis, the coupling of frequencies causes resonance and flips the nuclear spins of the excess population from the...

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Updated: Jul 19, 2025

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Training Dogs for Awake, Unrestrained Functional Magnetic Resonance Imaging

Published on: October 13, 2019

Magnetic Resonance Parameter Mapping using Self-supervised Deep Learning with Model Reinforcement.

Wanyu Bian¹, Albert Jang¹, Fang Liu¹

¹Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129 USA.

|August 7, 2023

Summary

This summary is machine-generated.

RELAX-MORE, a new self-supervised learning method, enables fast and accurate quantitative MRI reconstruction. This subject-specific approach requires minimal data, improving MR parameter mapping efficiency and robustness.

Keywords:

Model reinforcement Optimization Quantitative MRI Self-supervised learning

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Last Updated: Jul 19, 2025

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Area of Science:

Medical Imaging
Machine Learning
Biophysics

Background:

Quantitative MRI (qMRI) is crucial for precise tissue characterization.
Current qMRI reconstruction methods face challenges with speed, accuracy, and data requirements.
Accelerated imaging in qMRI often leads to artifacts and reduced image quality.

Conclusions:

RELAX-MORE offers a feasible and effective self-supervised learning solution for rapid MR parameter mapping.
The subject-specific nature and minimal data requirement enhance the practical applicability of qMRI.
This method holds significant potential for advancing the clinical translation of quantitative MRI techniques.